The field of image resolution enhancement (also referred to as image interpolation, image upscaling, and image resampling) is very mature. In general, image resolution enhancement involves estimating unknown high-resolution information from low-resolution data based on both a model for the unknown high-resolution data, and a model for the image sampling process in both low-resolution and high-resolution image spaces. Some classical examples of data models are piecewise polynomial like in nearest-neighbor, bilinear, bicubic, and spline interpolation. The two principal models for image sampling are point sampling and area-sampling (i.e., integration over a “sensor” area around each sampling point). For each combination of a data model and sampling model, a large variety of solutions have been proposed for different combinations of the required resolution enhancement factor (e.g., integer/non-integer, small (×2)/large (>10)), the image type (e.g., single type of text, graphics, or photographs, or mixed type), and on the computational complexity (e.g., linear/non-linear, recursive/non-recursive, and single-pass/iterative).
In many situations, the default solution for image resolution enhancement uses pixel replication, which is fast and provides inherent sharpness; but pixel replication gives rise to pixeling artifacts (referred to as “jaggies”). Classical higher-order methods such as bi-linear (BL) or bi-cubic (BC) interpolation provide smoother reconstruction with much less pixeling, but at the price of detail-loss and therefore are sub-optimal in combining de-pixeling and sharpness preservation. Although sharpness could be boosted to a level similar to pixel replication by post-sharpening (e.g., unsharp masking), the sharpening operation increases computational complexity and boosts the mild pixeling of bilinear interpolation back to a medium level, although less than pixel replication.
What are needed are improved systems and methods of image resolution enhancement that provide de-pixeling and detail preservation with reduced computational complexity.